My analysis focuses on the relationship between income levels and access to public transportation to determine whether income disparities influence transit accessibility. I used the percentage of individuals living below twice the poverty level as an indicator of income levels, recognizing that the poverty threshold in New Jersey is lower than the federal standard. For income data, I relied on the American Community Survey (ACS) census data, provided in tabular format and aggregated by census tracts. Public transport data, including bus stops, bus lines, and rail lines, was sourced from the New Jersey Geographic Information Network (NJGIN) and provided in GeoJSON format. To align income data with geographic boundaries, I performed a spatial join to link poverty-level information to census tracts. For evaluating accessibility, I conducted a network analysis to generate a 15-minute walking buffer around transit stops. Additionally, I created buffer zones around bus lines to analyze coverage areas for public transportation. The poverty-level data from the ACS was deemed reliable and suitable for this analysis, while the GeoJSON transport data was appropriately structured for spatial operations, with no significant data quality issues. Overall, this methodology allowed for a thorough assessment of the relationship between income levels and access to public transit. | |
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Mrudhula Sai Boppey
Commandline GIS